AnyGoal: Vision-Language Guided Multi-Agent Exploration for Training-Free Lifelong Navigation
AnyGoal is a training-free multi-robot navigation architecture that uses a Vision-Language Model (VLM) for frontier-based exploration and coordinates agents via a shared 2D Gaussian Bayesian Value Map (BVM), achieving 52.4% subtask success rate on GOAT-Bench, a +27.5pp improvement over Modular GOAT.
[2606.13878] AnyGoal: Vision-Language Guided Multi-Agent Exploration for Training-Free Lifelong Navigation
[Submitted on 11 Jun 2026]
Title:AnyGoal: Vision-Language Guided Multi-Agent Exploration for Training-Free Lifelong Navigation
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Abstract:End-to-end navigation policies trained on large simulation corpora degrade sharply when transferred to out-of-distribution scenes, categories, or goal modalities. Modular pipelines such as Modular GOAT are bottlenecked by closed-set object detection recall, while 3D snapshot-memory systems (e.g. 3D-Mem) accumulate dense, view-dependent representations that are heavy to maintain. We present AnyGoal, a training-free multi-robot architecture that places a Vision-Language Model (VLM) at the core of frontier-based exploration and coordinates agents through a shared 2D Gaussian Bayesian Value Map (BVM). The BVM maintains a per-pixel (mu, sigma^2) posterior over goal relevance, updated via precision-weighted fusion of VLM scores through a depth-cone mask, and is never reset between subtasks, yielding lifelong evidence accumulation. Frontiers are ranked by a convex blend of a VLM-as-judge softmax and a Bayesian UCB term on the BVM. A greedy allocator with spatial-separation penalty and commitment hysteresis distributes frontiers across agents without a centralized controller. On the full GOAT-Bench val unseen split (360 episodes, 2,669 subtasks), our dual-agent system achieves 52.4% Subtask SR at 12.7% SPL--state of the art under the strict physical regime (discrete 0.25 m steps, no teleportation, 42 deg HFOV) and a +27.5 pp improvement over Modular GOAT (24.9%). Single-agent AnyGoal achieves 41.9% Subtask SR, showing gains arise from the decision architecture. A four-way perception ablation shows that open-vocabulary detectors shift the dominant failure mode from exploration to goal verification.
Comments: 17 pages, 3 figures
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2606.13878 [cs.RO]
(or arXiv:2606.13878v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.13878
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: MoniJesu James [view email] [v1] Thu, 11 Jun 2026 20:07:34 UTC (88 KB)
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